u/Practical_Sir8080

I recorded a rosbag from a simulation run where the robot behaved weirdly. The bag is about 15 GB.

I know I can replay it in RViz and inspect topics in Foxglove, but I’m wondering what people usually do after that (sorry new to this field).

Do you just inspect the log, find the bug, fix the code, and move on? Or is there a standard practice for keeping these logs and reusing them later?

What's with people on the platform matching, telling you how great your idea is and then ending the message asking for "fair" equity split and to be paid 3-4k USD a month? The number is always 3-4k USD a month. Is anyone else experiencing this?

YC keeps pushing the idea of AI-native companies.

I can see the pitch: tiny teams, ai agents doing ops, support, sales, admin, maybe even delivery.

But I’m curious from people actually building/running these:

What makes a company ai native?

Is it:

• AI doing the work?
• humans only handling exceptions?
• every process designed around agents?
• fewer employees?
• different margins?
• live human oversight?
• internal tools built around AI from day one?

Or is most of this still just normal businesses with some AI tools bolted on?

Would love concrete examples from people who’ve actually tried it.

Hey friends,
I’ve been building with AI coding tools for a while, and I keep running into a weird problem.

The current flow is usually:

1. Brainstorm idea in ChatGPT/Claude
2. Paste master prompt into Cursor
3. AI starts building
4. Keep saying “change this”, “fix that”, “add Stripe”, “redo auth”
5. Eventually the project gets messy and nobody really knows what the AI touched or why

Real engineering teams don’t work like this.

They have scopes, ownership, change requests, reviews, handoffs, approvals, and history.

I know some people will say: “Isn’t this what Cursor rules are for?”

Maybe partly. But Cursor rules feel more like static instructions:

• follow this architecture
• don’t touch this folder
• use this style
• run tests

What I’m thinking about is more like a live work-control layer before the AI starts changing things.

Example:

Instead of Cursor just being “Cursor”, it can act as:

• Architecture Agent
• Auth Agent
• Payments Agent
• Security Agent
• QA Agent

Each agent identity has a domain, permissions, and boundaries.

So if the Frontend Agent is adding a “Pay rent” button and suddenly tries to touch Stripe webhook logic, the system flags that as a department leap. It would need to handshake with the Payments Agent first. Risky stuff still needs human approval.

The difference I’m trying to test:

Cursor rules = general instructions inside one tool.

This idea = live work state:

• What change is being made?
• Which agent identity owns it?
• What is in scope?
• What is out of scope?
• What domains are risky?
• What needs review?
• What did the agent hand off when finished?
• What audit trail exists?
• Is another agent/session already working on this?

I’m not trying to replace GitHub, Jira, Linear, or PR review.... I’m wondering if there’s a missing layer before the AI acts.

Something that turns “big prompt into Cursor” into:

• scoped change request
• controlled work session
• domain boundaries
• handoff
• review requirement
• history/audit

Question for devs/builders:

Is this a real problem you can see emerging, or does it sound like over-engineering around bad AI workflow?

Would you use something that makes AI coding agents operate inside scoped work sessions instead of just relying on prompts and rules?

I’m not selling anything. I’m trying to sanity-check an idea before building more.

Context: Most gyms already track operational stuff (attendance, freezes, cancels, trials, etc.). What’s harder to see consistently is the human layer: who’s improving, who went quiet before they cancel, what equipment is quietly pissing people off, and what members would say if you didn’t run a formal survey.

The rough concept: members use a simple app to log workouts / PBs / light social stuff (think “Strava-ish but gym-first”). The owner/manager side wouldn’t be another giant analytics wall. It would be closer to a weekly briefing: who deserves recognition, who may be drifting early, where friction is building (repairs, always-busy kit, broken kit), short member voice signals (one-tap “all good / issue / improve”), and a short “do this week” list e.g. (message Jake, fix treadmill #4, run a cable challenge, etc.). Optional peer benchmarks later, only if the core briefing is useful.

What I’m trying to learn from you

1. If you got one page a week with 5-7 concrete actions, would you actually use it, or would it sit unread next to everything else you already get?
2. What’s actually missing from what you use today? Not “more dashboards,” but the one sentence you wish you knew each Monday.
3. What would make this feel creepy or unfair to members or staff? (I want to avoid surveillance vibes.)
4. Brutal take: is this useful for a busy GM, or just interesting?

Happy to take heat. If you want, I can share a mock screenshot of what the briefing could look like (still fake data, just layout). Otherwise ignore that and just roast the idea.

Thanks.

I built a lightweight ManyChat-style tool for Instagram DMs.

The basic idea: connect Instagram, set simple keywords, and route inbound DMs to the right action.

Added layers I’m testing:

- paid video consult links
- booking/deposit flows
- client intake forms
- request filtering
- simple dashboards for inbound requests

A few use cases:

- Fitness creator: someone DMs “form check” → gets a paid video consult link
- Beauty provider: someone DMs “lashes” → gets services, availability, and booking/deposit flow
- Coach/consultant: someone DMs “quick question” → gets a paid consult link or intake form
- Tradie: someone DMs “quote” → gets asked for suburb/photos/urgency and receives a job sheet

Currently being reviewed by Meta, so we’re waiting on final checks before wider access.

If this sounds useful, sign up and I’ll update you once it passes Meta review.